Ruminant methane emission modeling and monitoring system and method based on multi-mode breathing characteristics

Abstract

The invention discloses a ruminant methane emission modeling and monitoring system and method based on multi-mode breathing characteristics, and belongs to the technical field of animal husbandry environment engineering and greenhouse gas emission monitoring. The method comprises the following steps: in a feeding induction type sampling scene, capturing a discontinuous expiration pulse signal generated by a ruminant by using an expiration phase detection unit controlled by pressure-flow double feedback, and synchronously obtaining gas instantaneous flow data and methane concentration data; in combination with motion frequency spectrum information collected by a motion acceleration sensor arranged at the jaw of the ruminant, a respiratory frequency correction factor is extracted, and respiratory rhythm changes caused by ingestion or rumination behaviors are compensated; on the basis, time integration is carried out on gas flow and concentration data, and a dynamic calculation model of the methane emission flux is established. The system also reduces the influence of environmental disturbance on the monitoring result through multi-source gas sensing data fusion and sensor layout optimization, and is suitable for methane emission monitoring under the condition of short-time stay of ruminants.

Description

Technical Field

[0001] This invention relates to the field of animal greenhouse gas emission monitoring and flux calculation technology, specifically to a system and method for modeling and monitoring methane emissions from ruminants based on multimodal respiratory characteristics. Background Technology

[0002] Methane (CH4) is a significant greenhouse gas, and its global warming potential (GWP) is significantly higher than that of carbon dioxide (CO2) on the commonly used 100-year timescale. During the digestion of ruminants, rumen microorganisms ferment methane, which is primarily emitted into the environment as belching gas. Livestock methane emissions constitute a significant proportion of agricultural greenhouse gas emissions; therefore, accurate and repeatable monitoring and assessment of ruminant methane emissions are fundamental to the verification of emission reduction technologies, the development of green livestock farming, and related accounting work.

[0003] Existing technologies for monitoring methane emissions in ruminants include the respiration chamber method, the mask method, portable sampling and sensor detection methods, and feeding-induced "site-visit" monitoring systems. The respiration chamber method offers high accuracy but is costly, interferes with animal behavior, and has limited sample size; the mask method is prone to triggering stress responses; while portable sampling systems are highly adaptable, they are susceptible to environmental wind fields, changes in head position, and discontinuous sampling, resulting in insufficient temporal resolution and stability. In recent years, feeding-induced monitoring equipment has been developed to measure individual gas fluxes during voluntary feeding, typically supporting the measurement and aggregation of methane (CH4), carbon dioxide (CO2), and optionally oxygen (O2) and hydrogen (H2), offering good usability and scalability.

[0004] However, key challenges remain in feeding-induced, short-stay sampling scenarios: First, respiration during feeding and rumination is characterized by discontinuous pulses and is affected by background disturbances, making it difficult to accurately screen effective expiratory segments; second, flux is often simplified to a static estimate of "average concentration × average flow rate," which fails to reflect time-varying flux; third, feeding and rumination cause respiratory rhythm shifts, and lack of compensation easily leads to systematic errors; fourth, the complex three-dimensional flow field and turbulent disturbances in the sampling area, coupled with unoptimized sensor placement and triggering strategies, increase uncertainty. Therefore, a matching flux algorithm is needed that can run on feeding-induced devices, capable of screening coupled gas instantaneous flow rate and concentration data based on effective expiratory pulses, performing respiratory rhythm compensation, and combining three-dimensional flow field modeling and sampling layout optimization to improve the accuracy, stability, and repeatability of methane emission flux estimation under short-stay sampling conditions.

[0005] Based on this, the present invention proposes a modeling and monitoring system and method for methane emissions from ruminants based on multimodal respiratory characteristics. This system can be executed online by the controller of a feeding-induced monitoring device. During the monitoring period when the animal's head is inserted into the feeding cavity and remains in the sampling area, discontinuous expiratory pulse signals are captured, and instantaneous gas flow rate data and methane concentration data are acquired simultaneously. The respiratory frequency correction factor is determined by combining the jaw movement spectrum, and the real-time methane emission flux is calculated based on a dynamic gas flux integral model. The overall uncertainty is reduced by multi-source sensor fusion and sensor spatial layout optimization, thereby providing support for greenhouse gas emission reduction research and technology evaluation in ruminants. Summary of the Invention

[0006] The main objective of this invention is to propose a system and method for modeling and monitoring methane emissions from ruminants based on multimodal respiratory characteristics. This aims to improve the applicability, accuracy, and temporal resolution of methane emission flux monitoring schemes in feeding-induced sampling scenarios, enhance the ability to identify effective exhalation segments and dynamically model flux, and reduce the overall monitoring uncertainty through multi-source data fusion and sensor layout optimization.

[0007] In a first aspect, the present invention provides a method for modeling and monitoring methane emissions in ruminants based on multimodal respiratory characteristics. The method is executed by a controller of a feeding-induced ruminant methane emission modeling and monitoring system. The monitoring device includes: a feeding cavity for guiding ruminants to feed, a gas collection structure communicating with the feeding cavity, and multiple sensors communicatively connected to the controller. The multiple sensors include at least: an expiratory phase detection unit based on pressure-flow dual feedback control, a gas flow sensor, and a gas concentration sensor disposed within the feeding cavity; and a motion acceleration sensor disposed within the jaw of the ruminant to be tested. The method includes: When the ruminant inserts its head into the feeding cavity to eat, the exhalation phase detection unit detects and captures the discontinuous exhalation pulse signal generated by the ruminant in the sampling area. For each segment of discontinuous expiratory pulse signal captured by the gas flow sensor and the gas concentration sensor, the corresponding instantaneous gas flow data Q(t) and methane concentration data C(t) are acquired simultaneously. The respiratory rate correction factor f during the sampling period is extracted by analyzing the jaw movement spectrum collected by the motion acceleration sensor. R (t) to correct for the impact of respiratory rate deviations caused by feeding or rumination behavior on methane emission assessment; Based on the instantaneous gas flow rate data Q(t), methane concentration data C(t), and respiratory rate correction factor f R (t), establish a dynamic flux model ΦCH4 =∫[C(t)·Q(t)·f R (t)]dt To calculate the real-time methane emission flux during the monitoring period, wherein the integration interval covers the effective expiratory pulse period captured or the entire monitoring period; By combining the measurement data from the gas concentration sensor, and based on the sensor spatial layout and sampling strategy optimized by Monte Carlo simulation, combined with dynamically adjusted sampling control, heterogeneous data weighted fusion and spatial error correction, the methane flux monitoring results are output and its overall uncertainty is controlled.

[0008] In an optional implementation, the ruminant to be tested stays in the sampling area for 5 to 10 minutes.

[0009] In an optional implementation, the discontinuous expiratory pulse signal in step (1) includes an effective discontinuous expiratory pulse signal; wherein, the effective discontinuous expiratory pulse signal is the discontinuous expiratory pulse signal detected by the pressure-flow dual feedback control-based expiratory phase detection unit, and the instantaneous expiratory flow rate is in the range of 0.2 m / s to 0.5 m / s; and, the corresponding instantaneous gas flow rate data and methane concentration data are obtained only based on the effective discontinuous expiratory pulse signal.

[0010] In an optional implementation, the instantaneous gas flow rate data Q(t) in step (2) is measured in real time by a three-dimensional ultrasonic anemometer to obtain the gas velocity vector, and the velocity vector information is used to compensate for the interference of turbulence on the flow rate measurement; and when the ruminant puts its head into the feeding cavity to eat, the instantaneous gas velocity vector corresponding to each segment of the discontinuous exhalation pulse signal is obtained by the three-dimensional ultrasonic anemometer based on the discontinuous exhalation pulse signal.

[0011] In an optional embodiment, the gas concentration sensor in step (2) includes a dual-channel nondispersive infrared (NDIR) sensor, the concentration detection range of the NDIR sensor is 0 to 2000 ppm, and the detection accuracy is higher than ±2%FS; and when the ruminant inserts its head into the feeding cavity to eat, the carbon dioxide concentration data corresponding to each segment of the nondispersive exhalation pulse signal is obtained by the NDIR sensor according to the nondispersive exhalation pulse signal.

[0012] In an optional embodiment, the gas concentration sensor further includes a tunable semiconductor laser absorption spectroscopy (TDLAS) sensor, and the measurement data of the NDIR sensor and the TDLAS sensor are weighted and fused using a preset heterogeneous data weighting algorithm to obtain the methane concentration data.

[0013] In an optional implementation, before acquiring the instantaneous gas flow rate data and methane concentration data corresponding to each segment of the discontinuous expiratory pulse signal, the method further includes: constructing a corresponding three-dimensional respiratory flow field model based on the feeding cavity; determining multiple key calibration nodes based on the three-dimensional respiratory flow field model and a preset optimization algorithm; and setting the expiratory phase detection unit based on pressure-flow dual feedback control, the gas flow sensor, and the gas concentration sensor according to the key calibration nodes.

[0014] In an optional implementation, the plurality of key calibration nodes includes at least seven key calibration nodes, and the preset optimization algorithm is a Monte Carlo simulation algorithm.

[0015] Secondly, this invention provides a modeling and monitoring system for methane emissions from ruminants based on multimodal respiratory characteristics, comprising: an acquisition module, a determination module, and a calculation module; wherein, The acquisition module is used to acquire, based on the discontinuous expiratory pulse signal detected by the pressure-flow dual feedback control expiratory phase detection unit, the instantaneous gas flow rate data and methane concentration data corresponding to each segment of the discontinuous expiratory pulse signal through the gas flow sensor and the gas concentration sensor, respectively, when the ruminant inserts its head into the feeding cavity to eat. The determining module is used to determine the respiratory rate correction factor based on the motion spectrum signal collected by the motion acceleration sensor installed in the jaw of the ruminant. The calculation module is used to calculate the methane emission flux of the ruminant within a preset time period based on the instantaneous gas flow rate data, the methane concentration data, the respiratory rate correction factor, and a preset dynamic gas flux algorithm.

[0016] In an optional embodiment, the gas flow sensor and / or gas concentration sensor is configured to detect gaseous components including at least methane (CH4) and carbon dioxide (CO2), and optionally also includes the detection of oxygen (O2) and / or hydrogen (H2).

[0017] Compared with the prior art, the present invention achieves at least the following technical effects: (1) Combining dynamic flux integration with effective expiratory pulse screening significantly improves temporal resolution, accuracy and short-term sampling stability: This invention captures discontinuous expiratory pulse signals in a feeding-induced sampling scenario and simultaneously acquires instantaneous gas flow data Q(t) and methane concentration data C(t). By screening effective expiratory pulse segments and introducing a dynamic flux integration model for calculation, the static estimation error of the traditional "average concentration × average flow" is avoided. This allows for a more realistic representation of the time-varying characteristics of flux within a short residence window, thereby significantly improving the temporal resolution and accuracy of methane emission measurement. At the same time, without increasing the sampling duration, it increases the proportion of effective data and the robustness of integration, thereby improving the statistical stability of a single sampling.

[0018] (2) Respiratory rhythm compensation based on jaw movement spectrum to reduce systematic biases introduced by feeding / rumination behavior: This invention utilizes the spectral characteristics of jaw movement acceleration signals to extract respiratory frequency correction factor f. R (t) was used for dynamic compensation in the flux model to correct the impact of respiratory rate deviations caused by feeding or rumination behavior on methane emission assessment. This mechanism can effectively suppress the accumulation of systematic errors caused by changes in behavioral state, making flux results more stable and repeatable, and reducing the systematic error of respiratory rhythm fluctuations caused by feeding behavior on emission assessment.

[0019] (3) Multi-source sensor fusion and flow field / layout optimization synergy enhance anti-interference capability and reduce overall uncertainty: This invention optimizes and corrects the sensor spatial arrangement and sampling strategy by fusing multi-source gas concentration data (e.g., heterogeneous weighting of NDIR and TDLAS) and combining layout optimization strategies such as three-dimensional breathing flow field modeling and Monte Carlo simulation. This reduces measurement deviation caused by turbulence disturbance and unreasonable arrangement, enhances the system's anti-interference capability and data robustness, thereby reducing the overall uncertainty of flux output and improving the consistency and reliability of field applications. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A schematic diagram of a ruminant methane emission modeling and monitoring system based on multimodal respiratory characteristics provided in an embodiment of this application; Figure 2 A schematic flowchart of a method for modeling and monitoring methane emissions from ruminants based on multimodal respiratory characteristics, provided in an embodiment of this application; Figure 3 A schematic flowchart of a method for modeling and monitoring methane emissions from ruminants based on multimodal respiratory characteristics, provided in another embodiment of this application; Figure 4 A schematic flowchart illustrating a method for modeling and monitoring methane emissions from ruminants based on multimodal respiratory characteristics, provided in another embodiment of this application; Figure 5 An experimental result analysis diagram of Experiment 1 provided in an embodiment of this application; Figure 6 An analysis diagram of the experimental results of Experiment 2 provided in an embodiment of this application; Figure 7 An analysis diagram of the experimental results of Experiment 3 provided in an embodiment of this application; Figure 8 The structural framework diagram of the ruminant methane emission modeling and monitoring system based on multimodal respiratory characteristics provided in the embodiments of this application is shown. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0024] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0025] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0026] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0027] Current methane emission monitoring schemes for ruminants mainly include the breathing chamber method, portable sampling system method, mask method, and infrared gas detector method.

[0028] Among them, the breathing chamber method usually involves placing ruminants alone in a closed breathing chamber and continuously monitoring their exhaled gas samples by periodically collecting them using a gas chromatograph. Although this method can achieve high-precision measurements, it has problems such as high cost, significant interference with animal behavior, and limited sample size.

[0029] Portable systems collect gas samples by bringing a handheld device close to a ruminant. While this method is convenient to use, it lacks continuous monitoring capabilities and is greatly affected by environmental conditions.

[0030] The mask method involves wearing a special mask on a ruminant to continuously sample and analyze the gas exhaled by the ruminant at close range. However, due to the lack of comfort when wearing the special mask and the possibility of interfering with the animal's eating, it is easy to cause stress to the animal, which greatly limits the application of this method.

[0031] The infrared gas detector method determines methane concentration by measuring the absorption of infrared light of a specific wavelength within the detection range. Although this method is not difficult to implement, it cannot analyze oxygen and nitrogen in methane monitoring of ruminants, and it is also greatly affected by environmental conditions.

[0032] It is evident that existing solutions generally suffer from poor applicability and insufficient accuracy, and are deficient in dynamic modeling and expiratory rhythm compensation, resulting in low data temporal resolution and high overall uncertainty.

[0033] To address the aforementioned issues, this application proposes a modeling and monitoring system and method for methane emissions from ruminants based on multimodal respiratory characteristics. The aim is to improve the applicability, accuracy, and temporal resolution of methane emission monitoring schemes for ruminants in feeding-induced sampling scenarios, and to reduce overall uncertainty through multi-source data fusion and spatial optimization.

[0034] Figure 1 A schematic diagram of a ruminant methane emission modeling and monitoring system based on multimodal respiratory characteristics, provided in an embodiment of this application, is shown below. Figure 1 The monitoring system includes: a feeding cavity for guiding ruminants to eat, a gas collection structure connected to the feeding cavity, multiple sensors, and a controller; the multiple sensors include at least: an exhalation phase detection unit based on pressure-flow dual feedback control, a gas flow sensor, a gas concentration sensor, and a motion acceleration sensor located in the feeding cavity, and located in the jaw of the ruminant to be tested; the gas collection structure and each sensor are communicatively connected to the controller.

[0035] For example, the feeding cavity may be located within a semi-open shell, which may include an opening side. The feeding cavity may communicate with the outside through this opening side. The feeding cavity may contain a feed trough or bowl for holding feed. Ruminants may insert their heads into the feeding cavity through the opening side of the semi-open shell to consume the feed in the trough or bowl. The feed in the trough or bowl may be provided in a daily fixed quantity, such as 0.5~3.0 kg / day, but the specific quantity can be adjusted according to actual conditions and is not limited thereto. It is understood that the material, shape, and size of the semi-open shell, the size and shape of the opening side of the semi-open shell, and the type and composition of the feed can all be selected and determined according to actual needs, and are not limited herein.

[0036] The aforementioned pressure-flow dual feedback control-based expiratory phase detection unit, gas flow sensor, gas concentration sensor, and motion acceleration sensor located in the feeding cavity, as well as the ruminant's jaw, can each be provided in one or more forms. When multiple sensors of a certain type are provided, such as multiple pressure-flow dual feedback control expiratory phase detection units, the type and model of each sensor can be the same or different, and they can be located in different positions. However, the specific type, quantity, model, and location of each sensor can be adjusted and determined according to the actual situation, and are not limited here.

[0037] The aforementioned gas collector can be, for example, a negative pressure fan, but is not limited to a negative pressure fan. The air inlet of the gas collector is connected to the feeding chamber. For example, the air inlet of the gas collector can be connected to the feeding chamber via a pipe, or the air inlet of the gas collector can be directly connected to the feeding chamber, but the specific connection method is not limited to the examples mentioned above. The aforementioned gas collector may also have an air outlet, which can be connected to an external container via a pipe. The pipe of the gas collector may contain, for example, a filter device. The type and model of the gas collector, the size of the air inlet and outlet, the specifications and type of the pipe, and the structure and form of the filter device can all be selected and determined according to actual needs, and no specific restrictions are imposed here.

[0038] Exemplarily, the feeding cavity may also be equipped with a feeding-inducing structure to ensure that the ruminants under test can maintain relatively stable feeding behavior within the sampling area. In one embodiment of this application, the ruminants under test stay in the sampling area for 5 to 10 minutes to ensure the effectiveness of sampling and flux estimation. The number of sensors may be one or more, and their arrangement can be adjusted according to the structure of the feeding cavity and the flow field characteristics of the sampling area; the specific model, quantity, and location can be determined according to actual needs.

[0039] Figure 2 This is a schematic flowchart illustrating a method for dynamic modeling and real-time monitoring of rumen methane emissions in ruminants, provided in an embodiment of this application. The method is executed by the controller of the aforementioned monitoring system, such as... Figure 2 As shown, the method includes: S201. When the ruminant to be tested inserts its head into the feeding cavity to eat, the exhalation phase detection unit based on pressure-flow dual feedback control is used to detect and capture the discontinuous exhalation pulse signal generated by the ruminant in the sampling area; and for each segment of discontinuous exhalation pulse signal captured, the corresponding instantaneous gas flow rate data Q(t) and methane concentration data C(t) are simultaneously acquired through the gas flow sensor and gas concentration sensor, respectively.

[0040] For example, the expiratory phase detection unit of the pressure-flow dual feedback control described above can be for real-time monitoring. When a ruminant puts its head into the feeding cavity to eat, the ruminant's breathing, rumination, and eating can cause changes in the air pressure in the feeding cavity and generate airflow. When the expiratory phase detection unit of the pressure-flow dual feedback control detects the change in air pressure in the feeding cavity and the generation of airflow, it can generate a discontinuous expiratory pulse signal as the air pressure changes and airflow is generated, and send the discontinuous expiratory pulse signal to the controller through a communication connection with the controller.

[0041] When the controller receives a discontinuous expiratory pulse signal from the expiratory phase detection unit of the aforementioned pressure-flow dual feedback control, it can, for example, simultaneously control the gas flow sensor to acquire the instantaneous gas flow data corresponding to each segment of the discontinuous expiratory pulse signal, and control the gas concentration sensor to acquire the methane concentration data corresponding to each segment of the discontinuous expiratory pulse signal. It can be understood that the instantaneous gas flow data and methane concentration data corresponding to each segment of the discontinuous expiratory pulse signal can, for example, refer to the instantaneous gas flow data and methane concentration data corresponding to that segment of the discontinuous expiratory pulse signal acquired by the gas flow sensor and the gas concentration sensor at the instant the controller receives a segment of the discontinuous expiratory pulse signal. That is, the acquisition time of the instantaneous gas flow data and methane concentration data corresponding to the same segment of the discontinuous expiratory pulse signal is the same.

[0042] S202. Analyze the jaw movement spectrum signal collected by the motion acceleration sensor installed in the jaw of the ruminant to be tested, and extract the respiratory rate correction factor f during the sampling period. R (t) is used to correct for the impact of respiratory rate deviations caused by feeding or rumination behavior on methane emission assessment.

[0043] For example, the aforementioned motion acceleration sensor can be an accelerometer and gyroscope fixed to the jaw of a ruminant by means of adhesive, collar / headgear, etc. This fixing of the motion acceleration sensor can occur, for example, before the ruminant inserts its head into the feeding cavity to eat. The motion acceleration sensor can, for example, collect the motion acceleration and direction of the ruminant's jaw, and transmit the collected motion acceleration data of the ruminant's jaw as a motion spectrum signal to the controller via a communication connection. Upon receiving the motion spectrum signal, the controller can, for example, adjust the response using a preset respiratory rate correction factor f. R (t) Determine the algorithm or rule to determine the respiratory rate correction factor f R (t), but the specific preset respiratory rate correction factor f R (t) The content of the algorithm or rule determination is not limited here and can be adjusted according to the actual situation. The respiratory rate correction factor f mentioned above... R (t) For example, it can be used to correct for the impact of respiratory rate deviations caused by ruminant feeding or rumination behavior on methane emission assessments.

[0044] Optionally, in addition to determining the respiratory rate correction factor f based on the motion spectrum signal from the aforementioned motion acceleration sensor, R(t) In addition, the instantaneous gas flow data and methane concentration data corresponding to each segment of the above-mentioned discontinuous exhalation pulse signal are obtained through the above-mentioned gas flow sensor and the above-mentioned gas concentration sensor. For example, a dynamic background concentration correction algorithm can be introduced based on the background gas detection channel. The local background concentration sequence is reconstructed by sliding window averaging and spline interpolation. A reference baseline is dynamically established based on the 30-second window range before and after sampling to eliminate the error drift caused by the sudden change in background CH4 and CO2 concentration. Of course, the specific calculation principle and calculation steps of the above-mentioned dynamic background concentration correction algorithm can be adjusted and determined according to the actual situation, and are not limited here.

[0045] S203. Based on the instantaneous gas flow rate data Q(t), methane concentration data C(t), and respiratory rate correction factor f R (t) A dynamic flux model is established to calculate the real-time methane emission flux during the monitoring period, wherein the integral interval covers the effective expiratory pulse period or the entire monitoring period.

[0046] For example, the above-mentioned instantaneous gas flow rate data, methane concentration data, and respiratory rate correction factor f R (t) and a dynamic flux model are used to calculate the methane emission flux of the ruminants within a preset time period. For example, a dynamic methane flux calculation model can be constructed based on the instantaneous gas flow data, the methane concentration data, the respiratory rate correction factor, and the dynamic flux model. The preset dynamic gas flux algorithm can be expressed by the following formula (that is, the dynamic methane flux calculation model can be expressed by the following formula): Φ CH4 =∫[C(t)·Q(t)·f R dt, Among them, the above Φ CH4 This refers to the methane emission flux mentioned above, where C(t) is the methane concentration data at time t, Q(t) is the instantaneous gas flow rate data at time t, and f... R (t) is the respiratory rate correction factor at time t. It can be understood that the above Φ CH4 It is also the methane emission flux at time t, and the integral interval of the above ∫[]dt covers all valid expiratory pulse signal segments detected during the sampling period, ensuring that the model captures dynamic flux changes throughout the entire time period.

[0047] Furthermore, considering background concentration, we can also have:

[0048] Among them, the above With Φ CH4 The meaning remains the same, still referring to the methane emission flux at time t. Let be the methane volume fraction detected in real time at time t, and the above... The background concentration at time t is obtained by dynamically updating the concentration data of ruminants when they are not in the feeding cavity.

[0049] In practical applications, the methane emission flux mentioned above, in addition to the calculated methane emission flux, can also include a flux compensation term obtained based on behavioral and metabolic modeling. In this case, we have: Φ total (t) = Φ measured (t) +ΔΦ predicted (t), Among them, the above Φ total (t) represents the final methane emission flux at time t, and the above Φ measured (t) is the above or Φ CH4 That is, the methane emission flux at time t calculated above, and the aforementioned ΔΦ predicted (t) represents the flux compensation term obtained from behavioral and metabolic modeling at time t, used to correct the underestimation caused by short-term sampling omissions. This ΔΦ predicted The specific value of (t) can be determined by calculation or experimental simulation based on the actual situation, and is not restricted here.

[0050] The method for modeling and monitoring methane emissions from ruminants based on multimodal respiratory characteristics provided in this application captures discontinuous expiratory pulse signals from ruminants in real time through an expiratory phase detection unit based on pressure-flow dual feedback control installed in the feeding chamber. Instantaneous gas flow and methane concentration data synchronized with the discontinuous expiratory pulse signals are collected by gas flow and gas concentration sensors installed in the feeding chamber. A respiratory frequency correction factor determined by a motion spectrum signal collected by a motion acceleration sensor installed in the ruminant's jaw, along with a preset dynamic gas flux algorithm, is used to construct a dynamic methane flux calculation model. This model is then used to calculate the methane emission flux from ruminants within a preset time period. Compared to traditional monitoring methods, this method introduces several innovative technologies, including dynamic respiratory sampling, flow-concentration coupled modeling, respiratory rhythm compensation, multi-source heterogeneous data fusion, and rumen metabolic characteristic emission tracing. These improvements enhance the applicability, accuracy, and temporal resolution of the ruminant methane emission monitoring scheme, strengthen emission path identification capabilities, and reduce overall monitoring uncertainty.

[0051] Furthermore, in the above Figure 2Based on the embodiments, the discontinuous expiratory pulse signal in step (1) may include an effective discontinuous expiratory pulse signal. For example, the effective discontinuous expiratory pulse signal is a discontinuous expiratory pulse signal detected by the expiratory phase detection unit where the instantaneous expiratory flow rate is in the range of 0.2 m / s to 0.5 m / s; and, the corresponding instantaneous gas flow rate data and methane concentration data are obtained only based on the effective discontinuous expiratory pulse signal, thereby improving data reliability and reducing the impact of background disturbances.

[0052] The above-mentioned acquisition of instantaneous gas flow rate data and methane concentration data corresponding to each segment of the discontinuous expiratory pulse signal from the expiratory phase detection unit based on pressure-flow dual feedback control, through the gas flow sensor and the gas concentration sensor respectively, may include: Based on the effective discontinuous expiratory pulse signal of the expiratory phase detection unit based on pressure-flow dual feedback control, the instantaneous gas flow rate data and methane concentration data corresponding to each segment of the effective discontinuous expiratory pulse signal are obtained by the gas flow sensor and the gas concentration sensor, respectively.

[0053] In other words, a valid discontinuous expiratory pulse signal is generated only when the expiratory phase detection unit based on pressure-flow dual feedback control detects a change in air pressure within the feeding chamber and generates airflow with a velocity-flow rate of 0.2 m / s to 0.5 m / s. Only when the controller receives the valid discontinuous expiratory pulse signal from the pressure-flow dual feedback control expiratory phase detection unit does it control the gas flow sensor and the gas concentration sensor to acquire the instantaneous gas flow rate data and methane concentration data corresponding to each segment of the valid discontinuous expiratory pulse signal. This ensures the reliability of the collected instantaneous gas flow rate data and methane concentration data.

[0054] Optionally, the gas flow sensor described above can be a three-dimensional ultrasonic anemometer. The method may further include: When the ruminant inserts its head into the feeding cavity to feed, the gas instantaneous velocity vector corresponding to each segment of the non-continuous expiratory pulse signal is obtained by the three-dimensional ultrasonic anemometer based on the non-continuous expiratory pulse signal of the expiratory phase detection unit based on pressure-flow dual feedback control.

[0055] For example, the instantaneous gas velocity vector corresponding to each segment of the above-mentioned discontinuous expiratory pulse signal can be used to compensate for the interference of turbulence on flow measurement, but the specific compensation calculation method can be adjusted and determined according to the actual situation, and is not limited here.

[0056] Based on the above, the data collected by the above sensors can be considered as valid data if they meet the following conditions: (1) the ruminant stays in the feeding cavity for 5 to 10 minutes; (2) the peak frequency of the ruminant's expiratory cycle is 0.5 to 1.2 times / min; (3) the artifact removal amplitude of the motion acceleration of the ruminant's jaw collected by the motion acceleration sensor is set to not exceed 3g variation; (4) the wind speed deflection angle determined based on the instantaneous gas velocity vector must not exceed 45°. However, it is understood that the above conditions for valid data are only an example, and the actual conditions for valid data are not limited to the conditions in the above examples.

[0057] In addition, in the above Figure 1 Based on the embodiments, the gas concentration sensor described above may include: a dual-channel non-dispersive infrared (NDIR) sensor, wherein the concentration detection range of the NDIR sensor is 0~2000ppm, and the detection accuracy of the NDIR sensor is higher than ±2%FS. The method described above may further include: When the ruminant inserts its head into the feeding cavity to eat, the carbon dioxide concentration data corresponding to each segment of the non-continuous expiratory pulse signal is obtained by the NDIR sensor based on the non-continuous expiratory pulse signal of the expiratory phase detection unit based on pressure-flow dual feedback control.

[0058] For example, the aforementioned NDIR sensor may be a dual-channel NDIR sensor. The acquisition of carbon dioxide concentration data corresponding to each segment of the discontinuous expiratory pulse signal by the NDIR sensor based on the discontinuous expiratory pulse signal of the pressure-flow dual feedback control expiratory phase detection unit may be performed synchronously with the acquisition of methane concentration data corresponding to each segment of the discontinuous expiratory pulse signal by the NDIR sensor based on the discontinuous expiratory pulse signal of the pressure-flow dual feedback control expiratory phase detection unit.

[0059] In addition to the methane and carbon dioxide concentration data corresponding to each segment of the discontinuous expiratory pulse signal obtained by the NDIR sensor based on the discontinuous expiratory pulse signal of the expiratory phase detection unit based on pressure-flow dual feedback control when the ruminant has its head inserted into the feeding cavity to feed, the dual-channel NDIR sensor can also collect methane and carbon dioxide concentration data in the feeding cavity when the ruminant is not in the feeding cavity, as the background concentration at time t in the aforementioned embodiment. .

[0060] Figure 3 This is a schematic flowchart illustrating a method for dynamic modeling and real-time monitoring of rumen methane emissions in ruminants, provided in another embodiment of this application. Please refer to... Figure 3Furthermore, based on the above embodiments, the gas concentration sensor further includes a tunable semiconductor laser absorption spectroscopy (TDLAS) sensor. The acquisition of methane concentration data via the gas concentration sensor includes: S301. Based on the discontinuous expiratory pulse signal of the expiratory phase detection unit based on pressure-flow dual feedback control, the methane concentration data of the NDIR sensor and the methane concentration data of the TDLAS sensor corresponding to each segment of the discontinuous expiratory pulse signal are obtained through the NDIR sensor and the TDLAS sensor, respectively.

[0061] For example, the acquisition of methane concentration data from the NDIR sensor and the TDLAS sensor for each segment of the discontinuous expiratory pulse signal based on the pressure-flow dual feedback control expiratory phase detection unit can be performed simultaneously. That is, for each segment of the discontinuous expiratory pulse signal, the corresponding NDIR sensor methane concentration data is acquired simultaneously through the NDIR sensor and the corresponding TDLAS sensor methane concentration data is acquired simultaneously through the TDLAS sensor.

[0062] S302. Based on the methane concentration data from the NDIR sensor, the methane concentration data from the TDLAS sensor, and the preset heterogeneous data weighting algorithm, calculate and obtain the methane concentration data.

[0063] For example, the methane concentration data calculated based on the NDIR sensor methane concentration data, the TDLAS sensor methane concentration data, and a preset heterogeneous data weighting algorithm can, for instance, refer to calculating the methane concentration data of a segment of discontinuous expiratory pulse signal using the NDIR sensor methane concentration data and the TDLAS sensor methane concentration data, respectively, through a preset heterogeneous data weighting algorithm. In other words, the methane concentration data of that segment of discontinuous expiratory pulse signal is the result of a heterogeneous data weighting calculation of the NDIR sensor methane concentration data and the TDLAS sensor methane concentration data for that segment of discontinuous expiratory pulse signal. The specific algorithm content of the preset heterogeneous data weighting algorithm can be adjusted and determined according to actual needs, and is not specifically limited here.

[0064] Figure 4 This is a schematic flowchart of a method for dynamic modeling and real-time monitoring of rumen methane emissions in ruminants, provided in another embodiment of this application. Figure 4 As shown above, in the aforementioned Figure 2Based on the embodiments, before acquiring the instantaneous gas flow rate data and methane concentration data corresponding to each segment of the discontinuous expiratory pulse signal through the gas flow sensor and the gas concentration sensor respectively, according to the discontinuous expiratory pulse signal of the expiratory phase detection unit based on pressure-flow dual feedback control, the method may further include: S401. Construct a corresponding three-dimensional respiratory flow field model based on the above feeding cavity.

[0065] For example, the three-dimensional respiratory flow field model constructed based on the feeding cavity can be implemented by, for example, a simulation platform. The three-dimensional respiratory flow field model can be used to represent the airflow process generated in the feeding cavity due to the ruminant's breathing, rumination, and eating when the ruminant puts its head into the feeding cavity to eat.

[0066] S402. Based on the above three-dimensional respiratory flow field model and preset optimization algorithm, determine several key calibration nodes.

[0067] For example, the aforementioned key calibration nodes may refer to points on the main paths of airflow generated by ruminants' breathing, rumination, and feeding in the aforementioned three-dimensional respiratory flow field model.

[0068] S403. Based on the above key calibration nodes, set up the above-mentioned expiratory phase detection unit based on pressure-flow dual feedback control, the above-mentioned flow sensor and the above-mentioned concentration sensor.

[0069] For example, the above-mentioned setting of the expiratory phase detection unit based on pressure-flow dual feedback control, the flow sensor, and the concentration sensor according to the above-mentioned key calibration node can, for example, mean setting the expiratory phase detection unit based on pressure-flow dual feedback control, the flow sensor, and the concentration sensor at the above-mentioned key calibration node to optimize the sensor spatial layout and thus optimize the sensor sampling triggering strategy and sampling accuracy.

[0070] Furthermore, in the above Figure 4 Based on the embodiments, the above-mentioned multiple key calibration nodes may include at least 7 key calibration nodes, and the above-mentioned preset optimization algorithm may be a Monte Carlo simulation algorithm.

[0071] Of course, the specific method for simulating and optimizing the sensor spatial layout using the Monte Carlo simulation algorithm can be adjusted and determined based on different simulation platforms and other factors. The specific optimization process and parameter debugging process are not limited here.

[0072] To verify the effectiveness, adaptability, and accuracy of the above-mentioned method for modeling and monitoring methane emissions from ruminants based on multimodal respiratory characteristics, experiments can be conducted in actual farming scenarios. For example, such experiments may include: Experiment 1: The subjects were three 0.85-year-old Simmental crossbred bulls (ruminants, referred to as cattle in Experiment 1), which were in their rapid growth phase. The test environment was a moderately cold winter condition with a temperature of approximately 14°C, and the animals were in the transitional phase from early morning rest to peak feeding time.

[0073] The ruminant methane emission modeling and monitoring system based on multimodal respiratory characteristics in this experiment can be deployed in the sampling channel inside the pen. The system can also include an infrared recognition module and a non-contact positioning unit to detect cattle entering the feeding cavity area. After detecting cattle entering the feeding cavity area, the exhalation sampling process corresponding to the ruminant methane emission modeling and monitoring method based on multimodal respiratory characteristics is started.

[0074] In this experiment, the gas collector can be equipped with a conical airflow guide structure and a flow stabilizing plate at its front end to improve exhaled gas capture efficiency and suppress background gas interference. Simultaneously, the acceleration signal of the cattle's jaw and changes in exhaled gas concentration are acquired, enabling real-time coupled analysis of respiratory rhythm and behavioral state.

[0075] Furthermore, in this experiment, for example, a correction factor based on behavior state switching can be used to correct the original flux estimation result. The correction factor value used is 0.953, which is used to correct the flux overestimation bias caused by behavior frequency and measurement delay. Figure 5 An analysis graph of the experimental results of Experiment 1 provided in an embodiment of this application is shown below. Figure 5 As shown, the average methane emission concentration of this breed of cattle was 138.93 ppm when standing quietly, and increased to 490.25 ppm during the feeding phase. The fluctuation of gas flux was highly correlated with the head and neck acceleration signal (Pearson r=0.87).

[0076] By curve fitting of gas concentration-flow rate synchronous data from multiple respiratory cycles of cattle, a typical bimodal expiratory rhythm was extracted. This verified that the ruminant methane emission modeling and monitoring system based on multimodal respiratory characteristics has the ability to accurately identify methane release structures under natural conditions and demonstrates good stability, rhythm recognition ability, and measurement accuracy in deployment among medium and large ruminants.

[0077] Experiment 2: The test subjects were three 3.5-month-old male Hu sheep lambs (ruminants, referred to as sheep in Experiment 2). The ambient temperature during the monitoring was 24℃, which was in the mild autumn climate.

[0078] The ruminant methane emission modeling and monitoring system based on multimodal respiratory characteristics in this experiment could also include a Time-of-Flight (TOF) depth vision module to track changes in sheep head posture in real time and collect triaxial jaw motion data using an accelerometer worn on the sheep's jaw. In this experiment, when executing the exhalation sampling procedure corresponding to the above method, a trained SVM (Support Vector Machine) model can be used to classify sheep behavior, enabling the identification and time-stamping of three states: rumination, feeding, and resting. The gas collector is fixed to the side of the fence in a non-contact, close-range manner to continuously monitor the sheep's exhalation process, avoiding interference with their behavior.

[0079] Figure 6 An analysis graph of the experimental results of Experiment 2 provided in an embodiment of this application is shown below. Figure 6 As shown, the methane concentration in the exhaled breath of sheep remained stable at 40 ppm during the rumination phase, with a peak surge reaching 66.85 ppm during grazing. Frequent behavioral switching caused significant fluctuations in flux estimation, with the untreated intraday flux variation coefficient reaching as high as 22.5%. To improve the temporal stability and accuracy of flux estimation, a behavior-driven correction model was used for dynamic compensation in this experiment. The correction factor was obtained by fitting behavioral intensity and rhythm frequency parameters, with a value of 0.594. After correction, the intraday flux variation coefficient decreased to 9.7%, improving measurement consistency.

[0080] This experiment further verified the adaptability of the ruminant methane emission modeling and monitoring system based on multimodal respiratory characteristics to small-sized ruminants and its ability to capture methane emission flux under unrestrained, free behavior conditions. It also showed that the proposed modified model has good error control and compensation capabilities in high behavioral disturbance situations.

[0081] Experiment 3 involved three 2.5-year-old lactating Holstein cows undergoing three days of continuous dynamic monitoring (ruminants, referred to as cattle in Experiment 3). The tests covered three typical time periods: morning, noon, and evening, with an average daily ambient temperature of approximately 26°C, typical of spring weather.

[0082] The exhalation sampling procedure corresponding to the ruminant methane emission modeling and monitoring method based on multimodal respiratory characteristics implemented in this experiment includes: (1) When the ruminant inserts its head into the feeding cavity to eat, the gas flow rate and methane concentration data corresponding to each segment of the non-contact expiratory pulse signal are obtained by the gas flow sensor and the gas concentration sensor, respectively, based on the non-contact expiratory pulse signal of the expiratory phase detection unit based on pressure-flow dual feedback control, thereby realizing non-contact detection of expiratory pulses when the animal's head approaches the sampling area. Specifically, a valid non-contact expiratory pulse signal is generated only when the expiratory phase detection unit based on pressure-flow dual feedback control detects a change in air pressure within the feeding cavity and generates airflow, and the wind speed and flow rate of the generated airflow reach 0.35 m / s.

[0083] (2) When the ruminant inserts its head into the feeding cavity to eat, according to the non-continuous expiratory pulse signal of the expiratory phase detection unit based on pressure-flow dual feedback control, the methane concentration data and carbon dioxide concentration data corresponding to each segment of the non-continuous expiratory pulse signal are obtained by the NDIR sensor, wherein the concentration detection range of the NDIR sensor is 0~2000ppm, and the detection accuracy of the NDIR sensor is higher than ±2%FS.

[0084] (3) Based on the motion spectrum signal of the motion acceleration sensor installed on the jaw of the cattle, the respiratory frequency correction factor is extracted by combining Fourier transform spectrum analysis to compensate for the systematic error caused by the change in respiratory rate under the states of feeding, rumination and other conditions on emission estimation.

[0085] (4) Based on the above-mentioned preset dynamic gas flux algorithm, a methane emission flux model is constructed using dynamic integration: Φ CH4 =∫[C(t)·Q(t)·f R dt, The integral interval covers all valid expiratory pulse signal segments detected during the sampling period, ensuring that the model captures dynamic flux changes throughout the entire time period.

[0086] Figure 7 An analysis graph of the experimental results of Experiment 3 provided in an embodiment of this application is shown below. Figure 7 As shown, influenced by the dual rhythms of feed intake and lactation, the peak methane concentration in the exhaled breath of cattle in this experiment reached 688.91 ppm during the morning peak feeding period, decreasing to 23.72 ppm during the afternoon resting and rumination period. After fusing and analyzing multi-source data using the LSTM (Long Short-Term Memory) deep modeling module, flux output showed a significant correlation with milk yield (R² = 0.85), and could reflect the rhythmic fluctuations in the daily methane emission intensity of cattle.

[0087] To enhance the model's robustness under high behavioral disturbances, this experiment also introduced a training-obtained respiratory rate correction factor, fR(t) = 0.939. This factor was calculated by comparing the actual behavioral rhythms (rumination frequency, feeding persistence) within the sampling period with the standard respiratory rhythm model. After adding this correction factor, flux fluctuations decreased by approximately 41.2%, effectively suppressing the sensitivity of flux data to changes in behavioral state. This aligns with the high metabolic emission characteristics of dairy cows during peak lactation and also verifies the effectiveness and stability of the ruminant methane emission modeling and monitoring system based on multimodal respiratory characteristics in real-time flux modeling, rhythm compensation, and multi-source data fusion for high-flux ruminants under unconstrained conditions.

[0088] Figure 8 This is a structural framework diagram of a ruminant methane emission modeling and monitoring system based on multimodal respiratory characteristics, provided in an embodiment of this application. The system can execute the aforementioned ruminant methane emission modeling and monitoring method based on multimodal respiratory characteristics. The device can be, for example, the controller of the aforementioned system, such as... Figure 8 As shown, the device may include: The acquisition module 810 is used to acquire the instantaneous gas flow rate data and methane concentration data corresponding to each segment of the non-continuous expiratory pulse signal based on the non-continuous expiratory pulse signal detected by the expiratory phase detection unit, respectively through the gas flow sensor and the gas concentration sensor, when the ruminant under test inserts its head into the feeding cavity to eat. The determination module 820 is used to determine the respiratory rate correction factor based on the motion spectrum signal collected by the motion acceleration sensor installed in the jaw of the ruminant. The calculation module 830 is used to calculate the methane emission flux of ruminants within a preset time period based on instantaneous gas flow data, methane concentration data, respiratory rate correction factor, and preset dynamic gas flux algorithm.

[0089] The method provided in this application captures the discontinuous expiratory pulse signals of ruminants in real time through an expiratory phase detection unit based on pressure-flow dual feedback control installed in the feeding chamber. It also collects instantaneous gas flow data and methane concentration data synchronized with the discontinuous expiratory pulse signals using a gas flow sensor and a gas concentration sensor installed in the feeding chamber. Combined with a respiratory frequency correction factor determined by a motion spectrum signal collected by a motion acceleration sensor installed in the ruminant's jaw, and a preset dynamic gas flux algorithm, a dynamic methane flux calculation model is constructed. This model is then used to calculate the methane emission flux of ruminants within a preset time period. Compared to traditional monitoring methods, this method introduces several innovative technologies, including dynamic respiratory sampling, flow-concentration coupling modeling, respiratory rhythm compensation, multi-source heterogeneous data fusion, and rumen metabolic characteristic emission tracing. These improvements enhance the applicability, accuracy, and temporal resolution of the ruminant methane emission monitoring scheme, strengthen emission path identification capabilities, and reduce overall monitoring uncertainty.

[0090] Optionally, the aforementioned discontinuous expiratory pulse signal may include: an effective discontinuous expiratory pulse signal. The effective discontinuous expiratory pulse signal is the discontinuous expiratory pulse signal detected by the expiratory phase detection unit based on pressure-flow dual feedback control when the airflow velocity is between 0.2 m / s and 0.5 m / s.

[0091] The acquisition module 810 is specifically used to acquire, through the gas flow sensor and the gas concentration sensor, the instantaneous gas flow data and the methane concentration data corresponding to each segment of the effective discontinuous expiratory pulse signal based on the effective discontinuous expiratory pulse signal of the pressure-flow dual feedback control expiratory phase detection unit.

[0092] Optionally, the gas flow sensor described above is a three-dimensional ultrasonic anemometer.

[0093] The aforementioned acquisition module 810 can also be used to acquire the instantaneous gas velocity vector corresponding to each segment of the aforementioned non-continuous expiratory pulse signal through the aforementioned three-dimensional ultrasonic anemometer, based on the non-continuous expiratory pulse signal of the aforementioned expiratory phase detection unit based on pressure-flow dual feedback control, when the aforementioned ruminant inserts its head into the aforementioned feeding cavity to eat.

[0094] Optionally, the gas concentration sensor includes a dual-channel non-dispersive infrared (NDIR) sensor, wherein the concentration detection range of the NDIR sensor is 0~2000ppm, and the detection accuracy of the NDIR sensor is higher than ±2%FS.

[0095] The aforementioned acquisition module 810 can also be used to acquire carbon dioxide concentration data corresponding to each segment of the discontinuous expiratory pulse signal through the aforementioned NDIR sensor when the aforementioned ruminant inserts its head into the aforementioned feeding cavity to eat, based on the discontinuous expiratory pulse signal of the aforementioned pressure-flow dual feedback control expiratory phase detection unit.

[0096] Optionally, the gas concentration sensor described above may also include a tunable semiconductor laser absorption spectroscopy (TDLAS) sensor.

[0097] The aforementioned acquisition module 810 is specifically used to acquire, based on the discontinuous expiratory pulse signal of the expiratory phase detection unit based on pressure-flow dual feedback control, the methane concentration data of the NDIR sensor and the methane concentration data of the TDLAS sensor corresponding to each segment of the discontinuous expiratory pulse signal through the aforementioned NDIR sensor and the aforementioned TDLAS sensor, respectively.

[0098] The aforementioned calculation module 830 is specifically used to calculate and obtain the aforementioned methane concentration data based on the aforementioned NDIR sensor methane concentration data, the aforementioned TDLAS sensor methane concentration data, and a preset heterogeneous data weighting algorithm.

[0099] Optionally, the above-mentioned ruminant methane emission modeling and monitoring system based on multimodal respiratory characteristics may further include: a layout module for constructing a corresponding three-dimensional respiratory flow field model based on the feeding cavity; determining multiple key calibration nodes based on the three-dimensional respiratory flow field model and a preset optimization algorithm; and setting the above-mentioned expiratory phase detection unit based on pressure-flow dual feedback control, the above-mentioned flow sensor, and the above-mentioned concentration sensor based on the key calibration nodes.

[0100] Optionally, the aforementioned multiple key calibration nodes include at least 8 key calibration nodes, and the aforementioned preset optimization algorithm is the Monte Carlo simulation algorithm.

[0101] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0102] The above description is merely a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural transformations made based on the inventive concept of this application and the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A method for modeling and monitoring methane emissions from ruminants based on multimodal respiratory characteristics, characterized in that, The method is executed by a controller of a feeding-induced ruminant methane emission modeling and monitoring device. The monitoring device includes: a feeding cavity for guiding ruminants to feed, a gas collection structure communicating with the feeding cavity, and multiple sensors communicatively connected to the controller. The multiple sensors include at least: an exhalation phase detection unit based on pressure-flow dual feedback control, a gas flow sensor, and a gas concentration sensor disposed within the feeding cavity, and a motion acceleration sensor disposed within the jaw of the ruminant under test. During the monitoring period when the head of the ruminant under test is inserted into the feeding cavity and remains in the sampling area, the method includes: (1) Dynamic respiratory sampling: When the ruminant inserts its head into the feeding cavity to eat, the exhalation phase detection unit detects and captures the discontinuous exhalation pulse signal generated by the ruminant in the sampling area. (2) Gas flow rate-concentration coupling acquisition: For each segment of discontinuous expiratory pulse signal captured by the gas flow sensor and the gas concentration sensor, the corresponding instantaneous gas flow data Q(t) and methane concentration data C(t) are acquired simultaneously. (3) Compensation for respiratory rhythm: The respiratory rate correction factor f during the sampling period is extracted by analyzing the jaw movement spectrum collected by the motion acceleration sensor. R (t) to correct for the impact of respiratory rate deviations caused by feeding or rumination behavior on methane emission assessment; (4) Dynamic modeling and calculation of methane emission flux Based on the instantaneous gas flow rate data Q(t), methane concentration data C(t), and respiratory rate correction factor f R (t), establish a dynamic flux model Φ CH4 =∫[C(t)·Q(t)·f R (t)]dt To calculate the real-time methane emission flux during the monitoring period, wherein the integration interval covers the effective expiratory pulse period captured or the entire monitoring period; (5) Multi-source sensor fusion and optimization By combining the measurement data from the gas concentration sensor, and based on the sensor spatial layout and sampling strategy optimized by Monte Carlo simulation, combined with dynamically adjusted sampling control, heterogeneous data weighted fusion and spatial error correction, the methane flux monitoring results are output and its overall uncertainty is controlled.

2. The method according to claim 1, characterized in that, The ruminants to be tested stayed in the sampling area for 5 to 10 minutes.

3. The method according to claim 1, characterized in that, The discontinuous expiratory pulse signal mentioned in step (1) includes a valid discontinuous expiratory pulse signal; wherein, the valid discontinuous expiratory pulse signal is the discontinuous expiratory pulse signal detected by the expiratory phase detection unit based on pressure-flow dual feedback control, and the instantaneous expiratory flow rate is in the range of 0.2m / s to 0.5m / s; and, the corresponding instantaneous gas flow rate data and methane concentration data are obtained only based on the valid discontinuous expiratory pulse signal.

4. The method according to claim 1, characterized in that, In step (2), the instantaneous gas flow rate data Q(t) is measured in real time by a three-dimensional ultrasonic anemometer to obtain the gas velocity vector, and the flow rate vector information is used to compensate for the interference of turbulence on the flow rate measurement. The method further includes: When the ruminant inserts its head into the feeding cavity to eat, the instantaneous gas velocity vector corresponding to each segment of the non-continuous expiratory pulse signal is obtained by the three-dimensional ultrasonic anemometer based on the non-continuous expiratory pulse signal of the expiratory phase detection unit based on pressure-flow dual feedback control.

5. The method according to claim 1, characterized in that, The gas concentration sensor mentioned in step (2) includes: a dual-channel non-dispersive infrared (NDIR) sensor, wherein the concentration detection range of the NDIR sensor is 0~2000 ppm, and the detection accuracy of the NDIR sensor is higher than ±2% FS; The method further includes: When the ruminant inserts its head into the feeding cavity to eat, the carbon dioxide concentration data corresponding to each segment of the non-continuous expiratory pulse signal is obtained by the NDIR sensor based on the non-continuous expiratory pulse signal of the expiratory phase detection unit based on pressure-flow dual feedback control.

6. The method according to claim 5, characterized in that, The gas concentration sensor further includes a tunable semiconductor laser absorption spectroscopy (TDLAS) sensor, and the measurement data of the NDIR sensor and the TDLAS sensor are weighted and fused using a preset heterogeneous data weighting algorithm to obtain the methane concentration data.

7. The method according to claim 1, characterized in that, Before acquiring the instantaneous gas flow rate data and methane concentration data corresponding to each segment of the discontinuous expiratory pulse signal from the pressure-flow dual feedback control expiratory phase detection unit via the gas flow sensor and the gas concentration sensor, respectively, the method further includes: A three-dimensional respiratory flow field model is constructed based on the feeding cavity; Based on the three-dimensional respiratory flow field model and the preset optimization algorithm, several key calibration nodes are determined; Based on the key calibration node, the pressure-flow dual feedback control expiratory phase detection unit, the gas flow sensor, and the gas concentration sensor are configured.

8. The method according to claim 7, characterized in that, The multiple key calibration nodes include at least 7 key calibration nodes, and the preset optimization algorithm is the Monte Carlo simulation algorithm.

9. A modeling and monitoring system for methane emissions from ruminants based on multimodal respiratory characteristics, characterized in that, include: The acquisition module is used to acquire the instantaneous gas flow rate data and methane concentration data corresponding to each segment of the discontinuous expiratory pulse signal detected by the expiratory phase detection unit based on pressure-flow dual feedback control when the ruminant inserts its head into the feeding cavity to eat. The determination module is used to determine the respiratory rate correction factor based on the motion spectrum signal collected by the motion acceleration sensor installed in the jaw of the ruminant. The calculation module is used to calculate the methane emission flux of the ruminant within a preset time period based on the instantaneous gas flow rate data, the methane concentration data, the respiratory rate correction factor, and a preset dynamic gas flux algorithm.

10. The ruminant methane emission modeling and monitoring system according to claim 9, characterized in that, The gas flow sensor and / or gas concentration sensor are configured to detect gaseous components including at least methane and carbon dioxide, and optionally also include the detection of oxygen and / or hydrogen.

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